Senior MLOps Engineer

Responsibilities

Kforce has a client seeking a Senior MLOps Engineer in Fort Lauderdale, FL to join a high-performing team focused on building and scaling enterprise machine learning platforms. This role is responsible for designing, deploying, and optimizing production-grade ML infrastructure that enables data science teams to efficiently move models from experimentation to production. The ideal candidate has deep experience with Databricks, Apache Spark, Python, and CI/CD practices, along with a strong understanding of the full machine learning lifecycle. This position offers the opportunity to support innovative initiatives involving real-time analytics, recommendation engines, customer personalization, and AI-powered applications. Responsibilities:

• Design, build, and maintain scalable machine learning pipelines on Databricks
• Deploy, monitor, and manage machine learning models in production environments
• Develop and maintain CI/CD pipelines for ML and data workflows
• Build and support batch, streaming, and real-time data pipelines
• Partner with Data Scientists to operationalize and optimize machine learning solutions
• Implement model versioning, experiment tracking, and reproducible ML processes
• Establish and promote ML engineering best practices, governance, and quality standards
• Monitor model performance, data quality, and drift while supporting automated retraining strategies
• Optimize distributed workloads for performance, scalability, and cost efficiency
• Contribute to platform architecture supporting low-latency model inference and scalable model serving

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience
• Strong experience with Databricks, including Workflows, MLflow, and Delta Lake
• Advanced expertise with Apache Spark for batch and streaming data processing
• Strong Python development skills with experience building production-quality applications
• Experience designing and implementing CI/CD pipelines for data and machine learning workloads
• Knowledge of machine learning lifecycle management, including training, deployment, monitoring, and retraining
• Experience building scalable and distributed data pipelines and ML systems
• Hands-on experience with real-time or streaming architectures
• Experience working in Azure cloud environments

Preferred Skills

• Snowflake
• Kubernetes
• Docker
• Terraform or other Infrastructure-as-Code tools
• Feature Store technologies
• Kafka or event-driven architectures
• Model serving frameworks and low-latency API development
• ELK Stack or similar monitoring and observability platforms
• A/B testing and experimentation frameworks
• Large Language Model (LLM) deployment and serving
• RBAC, security, and governance within data and ML platforms

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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